Ant colony algorithm and AI-Enabled Intelligent Evaluation Modeling System for English translation quality
TL;DR
The proposed AI-IEMS based on DynAc-Trans-ELSTM is a potential solution to the previous drawbacks of the models and will be more effective with regard to quality evaluation in a wider range of translation contexts.
Abstract
The evaluation of the quality of translation is an essential and important aspect in translation, which is mostly done by human judgment, whether in machine translation or human translation. Current automatic systems lack in capturing the subtleties of context, and the quality of the translation, which makes them not so scalable or accurate for real-world use. The research introduces a novel approach combining the Dynamic Ant Colony-Transformer-based Enhanced Long Short-Term Memory (DynAc-Trans-ELSTM) model with an AI-Enabled Intelligent Evaluation Modeling System (AI-IEMS) to assess the quality of English translations. The dataset is a large collection of parallel corpora, such as human-generated translations and expert evaluations from different translation platforms. Data pre-processing includes WordPiece Tokenization, BERT for subword text segmentation pre-processing and semantic learning. To extract, identify, and weight terms in the data set that are important, based on their frequency and importance, the term frequency-inverse document frequency (TF-IDF) is used. DynAc-Trans-ELSTM is a novel idea in the area of machine translation evaluation because it dynamically modifies the evaluation process by an intelligent model that is a combination of ACA, Transformer and ELSTM networks. The results show that DynAc-Trans-ELSTM shows better performance than all the baselines and with better accuracy (93.68%). The proposed AI-IEMS based on DynAc-Trans-ELSTM is a potential solution to the previous drawbacks of the models and will be more effective with regard to quality evaluation in a wider range of translation contexts.